POSTER: Intrusion Detection System for In-vehicle Networks using Sensor Correlation and Integration

被引：21

作者：

Li, Huaxin ^{[1
]}

Zhao, Li ^{[1
]}

Juliato, Marcio ^{[1
]}

Ahmed, Shabbir ^{[1
]}

Sastry, Manoj R. ^{[1
]}

Yang, Lily L. ^{[1
]}

机构：

[1] Intel Labs, Santa Clara, CA 95054 USA

来源：

CCS'17: PROCEEDINGS OF THE 2017 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY | 2017年

关键词：

vehicular security; in-vehicle intrusion detection system; cyber-physical security;

D O I：

10.1145/3133956.313884

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The increasing utilization of Electronic Control Units (ECUs) and wireless connectivity in modern vehicles has favored the emergence of security issues. Recently, several attacks have been demonstrated against in-vehicle networks therefore drawing significant attention. This paper presents an Intrusion Detection System (IDS) based on a regression learning approach which estimates certain parameters by using correlated/redundant data. The estimated values are compared to observed ones to identify abnormal contexts that would indicate intrusion. Experiments performed with real-world vehicular data have shown that more than 90% of vehicle speed data can be precisely estimated within the error bound of 3 kph. The proposed IDS is capable of detecting and localizing attacks in real-time, which is fundamental to achieve automotive security.

引用

页码：2531 / 2533

页数：3

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